Role of Omani Nurses in Implementing Patient-Centered Care in Primary Healthcare
Bibliographic record
Abstract
BACKGROUND: Patient-centered care (PCC) has gained global recognition as a highly effective model for improving healthcare system performance and patient outcomes. In Oman, the Ministry of Health (MOH) has acknowledged the paramount importance of transitioning from a provider-centered healthcare delivery approach to a PCC model. Nurses play a pivotal role in this, helping patients with chronic conditions develop health-promoting behaviors, enhance treatment adherence, and reduce the risks of increased morbidity and mortality. PURPOSE: This report underscores the indispensable role of primary healthcare (PHC) nurses in spearheading the implementation of PCC in Oman. Omani nurses, with their unique skills in patient care and dedication to enhancing healthcare, can significantly improve the quality and outcomes of patient care. METHODS: This study's insights were derived from expert opinions and reinforced by comprehensive online database searches (MEDLINE, CINAHL, PubMed, Web of Science, and Google Scholar) for relevant articles addressing the role of PHC nurses in PCC implementation. FINDINGS: Nurses play a central role in the successful implementation of PCC. Their responsibilities include, but are not limited to, regular patient engagement, problem-solving, retention, and adherence for patients with chronic conditions. Advancing PCC implementation in Oman requires PHC nurses to engage in these core activities, thereby contributing to improved healthcare services and patient outcomes. CONCLUSION: The PCC provides a robust framework for enhancing nurse-patient relationships and improving the quality of nursing care in PHC settings. Omani nurses should align with the Oman Health Vision 2050 strategies to establish policies that further PCC implementation. Integrating PCC into undergraduate and post-basic nursing education programs is essential. Continuous research on PCC in Oman, coupled with support from the MOH for nursing staff, may encourage all stakeholders to engage in the ongoing enhancement of healthcare quality.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".